Mortality salience enhances neural activities related to guilt and shame when recalling the past.

Mortality salience (MS) influences cognition and behavior. However, its effect on emotion (especially moral emotions) and the underlying neural correlates are unclear. We investigated how MS priming modulated guilt and shame in a later recall task using functional magnetic resonance imaging. The behavioral results indicated that MS increased self-reported guilt but not shame. The neural results showed that MS strengthened neural activities related to the psychological processes of guilt and shame. Specifically, for both guilt and shame, MS increased activation in a region associated with self-referential processing (ventral medial prefrontal cortex). For guilt but not shame, MS increased the activation of regions associated with cognitive control (orbitofrontal cortex) and emotion processing (amygdala). For shame but not guilt, MS decreased brain functional connectivity related to self-referential processing. A direct comparison showed that MS more strongly decreased a functional connectivity related to self-referential processing in the shame than in the guilt condition. Additionally, the activation of insula during MS priming was partly predictive of neural activities related to guilt and shame in the subsequent recall task. Our study sheds light on the psychological and neural mechanisms of MS effects on moral emotions and provides theoretical insights for enriching terror management theory.

Effects of Sound Interventions on the Mental Stress Response in Adults: Protocol for a Scoping Review.

BACKGROUND: Sound therapy methods have seen a surge in popularity, with a predominant focus on music among all types of sound stimulation. There is substantial evidence documenting the integrative impact of music therapy on psycho-emotional and physiological outcomes, rendering it beneficial for addressing stress-related conditions such as pain syndromes, depression, and anxiety. Despite these advancements, the therapeutic aspects of sound, as well as the mechanisms underlying its efficacy, remain incompletely understood. Existing research on music as a holistic cultural phenomenon often overlooks crucial aspects of sound therapy mechanisms, particularly those related to speech acoustics or the so-called "music of speech." OBJECTIVE: This study aims to provide an overview of empirical research on sound interventions to elucidate the mechanism underlying their positive effects. Specifically, we will focus on identifying therapeutic factors and mechanisms of change associated with sound interventions. Our analysis will compare the most prevalent types of sound interventions reported in clinical studies and experiments. Moreover, we will explore the therapeutic effects of sound beyond music, encompassing natural human speech and intermediate forms such as traditional poetry performances. METHODS: This review adheres to the methodological guidance of the Joanna Briggs Institute and follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist for reporting review studies, which is adapted from the Arksey and O'Malley framework. Our search strategy encompasses PubMed, Web of Science, Scopus, and PsycINFO or EBSCOhost, covering literature from 1990 to the present. Among the different study types, randomized controlled trials, clinical trials, laboratory experiments, and field experiments were included. RESULTS: Data collection began in October 2022. We found a total of 2027 items. Our initial search uncovered an asymmetry in the distribution of studies, with a larger number focused on music therapy compared with those exploring prosody in spoken interventions such as guided meditation or hypnosis. We extracted and selected papers using Rayyan software (Rayyan) and identified 41 eligible papers after title and abstract screening. The completion of the scoping review is anticipated by October 2024, with key steps comprising the analysis of findings by May 2024, drafting and revising the study by July 2024, and submitting the paper for publication in October 2024. CONCLUSIONS: In the next step, we will conduct a quality evaluation of the papers and then chart and group the therapeutic factors extracted from them. This process aims to unveil conceptual gaps in existing studies. Gray literature sources, such as Google Scholar, ClinicalTrials.gov, nonindexed conferences, and reference list searches of retrieved studies, will be added to our search strategy to increase the number of relevant papers that we cover. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54030.

Co-design personal sleep health technology for and with university students.

University students often experience sleep disturbances and disorders. Personal digital technologies present a great opportunity for sleep health promotion targeting this population. However, studies that engage university students in designing and implementing digital sleep health technologies are scarce. This study sought to understand how we could build digital sleep health technologies that meet the needs of university students through a co-design process. We conducted three co-design workshops with 51 university students to identify design opportunities and to generate features for sleep health apps through workshop activities. The generated ideas were organized using the stage-based model of self-tracking so that our findings could be well-situated within the context of personal health informatics. Our findings contribute new design opportunities for sleep health technologies targeting university students along the dimensions of sleep environment optimization, online community, gamification, generative AI, materializing sleep with learning, and personalization.

Prompt engineering for digital mental health: a short review.

Prompt engineering, the process of arranging input or prompts given to a large language model to guide it in producing desired outputs, is an emerging field of research that shapes how these models understand tasks, process information, and generate responses in a wide range of natural language processing (NLP) applications. Digital mental health, on the other hand, is becoming increasingly important for several reasons including early detection and intervention, and to mitigate limited availability of highly skilled medical staff for clinical diagnosis. This short review outlines the latest advances in prompt engineering in the field of NLP for digital mental health. To our knowledge, this review is the first attempt to discuss the latest prompt engineering types, methods, and tasks that are used in digital mental health applications. We discuss three types of digital mental health tasks: classification, generation, and question answering. To conclude, we discuss the challenges, limitations, ethical considerations, and future directions in prompt engineering for digital mental health. We believe that this short review contributes a useful point of departure for future research in prompt engineering for digital mental health.

When advisors do not know what is best for advisees: Uncertainty inhibits advice giving.

While seeking advice can be beneficial for advisees, advisors may not always possess the necessary knowledge to provide appropriate guidance. Poor-quality advice can mislead advisees rather than offering assistance. Despite the research interest in advisees, few studies have investigated advisors' psychological and behavioral responses, especially when they faced uncertainty regarding the optimal course of action for advisees. To fill this gap, we developed novel paradigms aiming at manipulating advisors' uncertainty, allowing for a systematic investigation of advisors' behavior, motivation, and emotion. Across four studies, we consistently found that advisors under uncertainty give less advice. Furthermore, we observed that uncertainty modulates advisors' motivation to influence, worry about harm to others, and/or sense of power. The motivation to influence and/or worry about harm to others can mediate the effect of uncertainty on advice giving. Besides, we identified nuanced distinctions in the effects of ambiguity and risk, two distinct types of uncertainty, on advisors' psychological processes. Our findings shed light on advisors' self-monitoring of the quality of their advice, thereby contributing to a deeper understanding of advisor-advisee communication from the perspective of advisors.

Distance and grid-like codes support the navigation of abstract social space in the human brain.

People form impressions about others during daily social encounters and infer personality traits from others' behaviors. Such trait inference is thought to rely on two universal dimensions: competence and warmth. These two dimensions can be used to construct a 'social cognitive map' organizing massive information obtained from social encounters efficiently. Originating from spatial cognition, the neural codes supporting the representation and navigation of spatial cognitive maps have been widely studied. Recent studies suggest similar neural mechanism subserves the map-like architecture in social cognition as well. Here we investigated how spatial codes operate beyond the physical environment and support the representation and navigation of social cognitive map. We designed a social value space defined by two dimensions of competence and warmth. Behaviorally, participants were able to navigate to a learned location from random starting locations in this abstract social space. At the neural level, we identified the representation of distance in the precuneus, fusiform gyrus, and middle occipital gyrus. We also found partial evidence of grid-like representation patterns in the medial prefrontal cortex and entorhinal cortex. Moreover, the intensity of grid-like response scaled with the performance of navigating in social space and social avoidance trait scores. Our findings suggest a neurocognitive mechanism by which social information can be organized into a structured representation, namely cognitive map and its relevance to social well-being.

Transforming waste brake pads from automobiles into Nano-Catalyst: Synergistic Fe-C-Cu triple sites for efficient fenton-like oxidation of organic pollutants.

The arbitrary disposal of used brake pads from motor vehicles has resulted in severe heavy metal pollution and resource wastage, highlighting the urgent need to explore the significant untapped potential of these discarded materials. In this study, The in-situ growth of highly dispersed Fe2O3 nanocrystals was achieved by simple oxidation annealing of brake pad debris(BPD). Interestingly, Cu remained unoxidized and acted as a "valence state transformation bridge of Fe2O3" to construct the "triple Fe-C-Cu sites". The Fenton degradation experiment of pollutants was conducted under constant temperature conditions at 40 °C, a stirring rate of 1300 rpm, a pH value of 3, a catalyst dosage of 0.5 g/L, pollutant dosage ranging from 50 to 400 mg/L, and H2O2 dosage of 0.25 g/L. Experimental results showed that BPD treated at 300 °C for 2 h exhibited optimal Fenton-like oxidation activity, achieving rapid degradation of over 90 % of refractory antibiotics, such as tetracycline and ciprofloxacin, in organic wastewater within 10 min. This remarkable performance was mainly attributed to the synergistic effect of "Fe-C-Cu triple sites", where the electron-donating role of C in the Fe-C and Cu-C interfaces facilitated the conversion of the Fe(III) to Fe(II) and Cu(II) to Cu(I). In addition, the ability of Cu2+ to accept electrons at the Fe-Cu interface promoted the transition from Fe (II) to Fe (III). This "balance of electron gain and loss" accelerated the interfacial electron transfer and the recycle of dual Fenton sites, Fe(II)/Fe(III) and Cu(I)/Cu(II), to generate more ·OH from H2O2. Therefore, this strategy of functionalizing BPD as Fenton-like catalysts without the addition of external Fe provides intriguing prospects for understanding the construction of Fe-based Fenton catalysts and resource utilization of Fe-containing solid waste materials.

Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening.

Objective: Sleep apnea is a common sleep disorder affecting a significant portion of the population, but many apnea patients remain undiagnosed because existing clinical tests are invasive and expensive. This study aimed to develop a method for easy sleep apnea screening. Methods: Three supervised machine learning algorithms, including logistic regression, support vector machine, and light gradient boosting machine, were applied to develop apnea screening models at two apnea-hypopnea index cutoff thresholds: ≥ 5 and ≥ 30 events/hours. The SpO2 recordings of the Sleep Heart Health Study database (N = 5786) were used for model training, validation, and test. Multiscale entropy analysis was performed to derive a set of multiscale attention entropy features from the SpO2 recordings. Demographic features including age, sex, body mass index, and blood pressure were also used. The dependency among the multiscale attention entropy features were handled with the independent component analysis. Results: For cutoff ≥ 5/hours, logistic regression model achieved the highest Matthew's correlation coefficient (0.402) and area under the curve (0.747), and reasonably good sensitivity (75.38%), specificity (74.02%), and positive predictive value (92.94%). For cutoff ≥ 30/hours, support vector machine model achieved the highest Matthew's correlation coefficient (0.545) and area under the curve (0.823), and good sensitivity (82.00%), specificity (82.69%), and negative predictive value (95.53%). Conclusions: Our models achieved better performance than existing methods and have the potential to be integrated with home-use pulse oximeters.

Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review.

BACKGROUND: Over the past few decades, there has been a rapid increase in the number of wearable sleep trackers and mobile apps in the consumer market. Consumer sleep tracking technologies allow users to track sleep quality in naturalistic environments. In addition to tracking sleep per se, some sleep tracking technologies also support users in collecting information on their daily habits and sleep environments and reflecting on how those factors may contribute to sleep quality. However, the relationship between sleep and contextual factors may be too complex to be identified through visual inspection and reflection. Advanced analytical methods are needed to discover new insights into the rapidly growing volume of personal sleep tracking data. OBJECTIVE: This review aimed to summarize and analyze the existing literature that applies formal analytical methods to discover insights in the context of personal informatics. Guided by the problem-constraints-system framework for literature review in computer science, we framed 4 main questions regarding general research trends, sleep quality metrics, contextual factors considered, knowledge discovery methods, significant findings, challenges, and opportunities of the interested topic. METHODS: Web of Science, Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Fitbit Research Library, and Fitabase were searched to identify publications that met the inclusion criteria. After full-text screening, 14 publications were included. RESULTS: The research on knowledge discovery in sleep tracking is limited. More than half of the studies (8/14, 57%) were conducted in the United States, followed by Japan (3/14, 21%). Only a few of the publications (5/14, 36%) were journal articles, whereas the remaining were conference proceeding papers. The most used sleep metrics were subjective sleep quality (4/14, 29%), sleep efficiency (4/14, 29%), sleep onset latency (4/14, 29%), and time at lights off (3/14, 21%). Ratio parameters such as deep sleep ratio and rapid eye movement ratio were not used in any of the reviewed studies. A dominant number of the studies applied simple correlation analysis (3/14, 21%), regression analysis (3/14, 21%), and statistical tests or inferences (3/14, 21%) to discover the links between sleep and other aspects of life. Only a few studies used machine learning and data mining for sleep quality prediction (1/14, 7%) or anomaly detection (2/14, 14%). Exercise, digital device use, caffeine and alcohol consumption, places visited before sleep, and sleep environments were important contextual factors substantially correlated to various dimensions of sleep quality. CONCLUSIONS: This scoping review shows that knowledge discovery methods have great potential for extracting hidden insights from a flux of self-tracking data and are considered more effective than simple visual inspection. Future research should address the challenges related to collecting high-quality data, extracting hidden knowledge from data while accommodating within-individual and between-individual variations, and translating the discovered knowledge into actionable insights.

Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults.

It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75-0.88) and lift (4.1-11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.

Testing the distributed representation hypothesis in object recognition in two open datasets.

Neural representation has long been thought to follow the modularity hypothesis, which states that each type of information corresponds to a specific brain area. Though supported by many studies, this hypothesis surfers the pitfall of inefficiency for information encoding. To overcome difficulties the modularity representation hypothesis faced, researchers have proposed that information may be distributed represented in a specific brain area. The distributed representation hypothesis along with the multi-variate pattern approaches have made great success in detecting representation patterns in the previous decade. However, this hypothesis implicitly requires that the pattern should be transformed in a consistent way with respect to all of the represented information in the specific brain area. And the accuracy and validity of this prediction have never been thoroughly tested. Here in the present study, we tested this prediction in two open datasets compiling the object recognition. We validated the distributed representation patterns in the lateral occipital complex/ventral temporal gyrus where all six classifiers were capable of predicting the correct category represented. Furthermore, we correlated the classifiers' decision function values to the bold signals and found that the decision function value of the logistic regression classifier was exclusively correlated with activities of the same brain area in both datasets. These results support the distributed representation hypothesis and suggest that our neural system may be embedded within the algorithm of a specific classifier.

Transcranial Direct Current Stimulation Over the Right Temporal Parietal Junction Facilitates Spontaneous Micro-Expression Recognition.

Micro-expressions are fleeting and subtle emotional expressions. As they are spontaneous and uncontrollable by one's mind, micro-expressions are considered an indicator of genuine emotions. Their accurate recognition and interpretation promote interpersonal interaction and social communication. Therefore, enhancing the ability to recognize micro-expressions has captured much attention. In the current study, we investigated the effects of training on micro-expression recognition with a Chinese version of the Micro-Expression Training Tool (METT). Our goal was to confirm whether the recognition accuracy of spontaneous micro-expressions could be improved through training and brain stimulation. Since the right temporal parietal junction (rTPJ) has been shown to be involved in the explicit process of facial emotion recognition, we hypothesized that the rTPJ would play a role in facilitating the recognition of micro-expressions. The results showed that anodal transcranial direct-current stimulation (tDCS) of the rTPJ indeed improved the recognition of spontaneous micro-expressions, especially for those associated with fear. The improved accuracy of recognizing fear spontaneous micro-expressions was positively correlated with personal distress in the anodal group but not in the sham group. Our study supports that the combined use of tDCS and METT can be a viable way to train and enhance micro-expression recognition.

A Multi-Level Classification Approach for Sleep Stage Prediction With Processed Data Derived From Consumer Wearable Activity Trackers.

Consumer wearable activity trackers, such as Fitbit are widely used in ubiquitous and longitudinal sleep monitoring in free-living environments. However, these devices are known to be inaccurate for measuring sleep stages. In this study, we develop and validate a novel approach that leverages the processed data readily available from consumer activity trackers (i.e., steps, heart rate, and sleep metrics) to predict sleep stages. The proposed approach adopts a selective correction strategy and consists of two levels of classifiers. The level-I classifier judges whether a Fitbit labeled sleep epoch is misclassified, and the level-II classifier re-classifies misclassified epochs into one of the four sleep stages (i.e., light sleep, deep sleep, REM sleep, and wakefulness). Best epoch-wise performance was achieved when support vector machine and gradient boosting decision tree (XGBoost) with up sampling were used, respectively at the level-I and level-II classification. The model achieved an overall per-epoch accuracy of 0.731 ± 0.119, Cohen's Kappa of 0.433 ± 0.212, and multi-class Matthew's correlation coefficient (MMCC) of 0.451 ± 0.214. Regarding the total duration of individual sleep stage, the mean normalized absolute bias (MAB) of this model was 0.469, which is a 23.9% reduction against the proprietary Fitbit algorithm. The model that combines support vector machine and XGBoost with down sampling achieved sub-optimal per-epoch accuracy of 0.704 ± 0.097, Cohen's Kappa of 0.427 ± 0.178, and MMCC of 0.439 ± 0.180. The sub-optimal model obtained a MAB of 0.179, a significantly reduction of 71.0% compared to the proprietary Fitbit algorithm. We highlight the challenges in machine learning based sleep stage prediction with consumer wearables, and suggest directions for future research.

Pretending to Be Better Than They Are? Emotional Manipulation in Imprisoned Fraudsters.

Fraud can cause severe financial losses and affect the physical and mental health of victims. This study aimed to explore the manipulative characteristics of fraudsters and their relationship with other psychological variables. Thirty-four fraudsters were selected from a medium-security prison in China, and thirty-one healthy participants were recruited online. Both groups completed an emotional face-recognition task and self-report measures assaying emotional manipulation, psychopathy, emotion recognition, and empathy. Results showed that imprisoned fraudsters had higher accuracy in identifying fear and surprise faces but lower accuracy in identifying happiness than controls (t = 5.26, p < 0.001; t = 2.38, p < 0.05; t = 3.75, p < 0.001). Significantly lower scores on non-prosocial factors on the Managing the Emotions of Others scale (MEOS) were found for imprisoned fraudsters, relative to controls (t = 3.21, p < 0.01). Imprisoned fraudsters had low scores in the assessment of psychopathy than the control group, especially Factor 1 (t = 2.04, p = 0.05). For empathy, imprisoned fraudsters had significantly higher scores in perspective-taking than controls (t = 2.03, p = 0.05). Correlation analyses revealed that psychopathic traits were positively correlated with non-prosocial factors in both groups. However, the relationships between emotional manipulation and emotional recognition and empathy were not consistent across the groups. The results suggest that fraudsters may pretend to be as prosocial as healthy controls, who had lower antisocial tendencies, normal empathy ability, and would like to manipulate others' emotions positively during social interaction.

Injectable microfluidic hydrogel microspheres based on chitosan and poly(ethylene glycol) diacrylate (PEGDA) as chondrocyte carriers.

Direct injection of chondrocytes in a minimally invasive way has been regarded as the significantly potential treatment for cartilage repair due to their ability to fill various irregular chondral defects. However, the low cell retention and survival after injection still limited their application in clinical transformation. Herein, we present chondrocyte-laden microspheres as cell carriers based on a double-network hydrogel by the combination of the chitosan and poly(ethylene glycol) diacrylate (PEGDA). The microfluidic technique was applied to prepare size-controllable chitosan/PEGDA hydrogel microspheres (CP-MSs) via the water-in-oil approach after photo-crosslinking and physical-crosslinking. The chondrocytes were laden on CP-MSs, which showed good cell viability and proliferation after long-term cell cultivation. The in vitro investigation further demonstrated that chondrocyte-laden CP-MSs were injectable and the cell viability was still high after injection. In particular, these cell-laden microspheres were self-assembled into a 3D cartilage-like scaffold by a bottom-up strategy based on cell-cell interconnectivity, which suggested that these injectable chondrocyte-laden microspheres showed potential applications as chondrocyte carriers for bottom-to-up cartilage tissue engineering.

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors.

BACKGROUND: It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. OBJECTIVE: This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. METHODS: A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night's sleep in participants' homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed-rank test was performed to investigate the effect of user-specific factors. RESULTS: Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics. CONCLUSIONS: Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Validity of Consumer Activity Wristbands and Wearable EEG for Measuring Overall Sleep Parameters and Sleep Structure in Free-Living Conditions.

Consumer sleep tracking technologies offer an unobtrusive and cost-efficient way to monitor sleep in free-living conditions. Technological advances in hardware and software have significantly improved the functionality of the new gadgets that recently appeared in the market. However, whether the latest gadgets can provide valid measurements on overall sleep parameters and sleep structure such as deep and REM sleep has not been examined. In this study, we aimed to investigate the validity of the latest consumer sleep tracking devices including an activity wristband Fitbit Charge 2 and a wearable EEG-based eye mask Neuroon in comparison to a medical sleep monitor. First, we confirmed that Fitbit Charge 2 can automatically detect the onset and offset of sleep with reasonable accuracy. Second, analysis found that both consumer devices produced comparable results in measuring total sleep duration and sleep efficiency compared to the medical device. In addition, Fitbit accurately measured the number of awakenings, while Neuroon with good signal quality had satisfactory performance on total awake time and sleep onset latency. However, measuring sleep structure including light, deep, and REM sleep remains to be challenging for both consumer devices. Third, greater discrepancies were observed between Neuroon and the medical device in nights with more disrupted sleep and when the signal quality was poor, but no trend was observed in Fitbit Charge 2. This study suggests that current consumer sleep tracking technologies may be immature for diagnosing sleep disorders, but they are reasonably satisfactory for general purpose and non-clinical use.

Are Proselfs More Deceptive and Hypocritical? Social Image Concerns in Appearing Fair.

Deception varies across individuals and social contexts. The present research explored how individual difference measured by social value orientations, and situations, affect deception in moral hypocrisy. In two experiments, participants made allocations between themselves and recipients with an opportunity to deceive recipients where recipients cannot reject their allocations. Experiment 1 demonstrated that proselfs were more deceptive and hypocritical than prosocials by lying to be apparently fair, especially when deception was unrevealed. Experiment 2 showed that proselfs were more concerned about social image in deception in moral hypocrisy than prosocials were. They decreased apparent fairness when deception was revealed and evaluated by a third-party reviewer and increased it when deception was evaluated but unrevealed. These results show that prosocials and proselfs differed in pursuing deception and moral hypocrisy social goals and provide implications for decreasing deception and moral hypocrisy.

Antioxidant action and protective and reparative effects of lentinan on oxidative damage in HaCaT cells.

BACKGROUND: Lentinus edodes is one of the largest edible fungi. Lentinan, extracted from its fruiting body has clinically significant anticancer, antibacterial, antiviral, and anticoagulant effects; however, its preventive effects on skin oxidative damage are unclear. AIMS: We aimed to evaluate the in vitro antioxidation capability of lentinan and its protective and reparative effects on a model of cell oxidative damage. METHODS: We evaluated the in vitro antioxidant potential of lentinan by assessing its free-radical quenching ability using DPPH and ABTS and superoxide anions. Using the HaCaT cell line as the experimental system, we tested the protective and reparative effects of lentinan on a model of H2 O2 -induced cellular oxidative damage through assessment of cell survival rate, malondialdehyde (MDA) content, and superoxide dismutase (SOD) activity. RESULTS: Lentinan displayed high antioxidant potential: DDPH and ABTS quenching rates were above 60%; superoxide anions, approximately 18%. Furthermore, lentinan could dose-dependently prevent the reduction of activity in HaCaT cells by H2 O2 , reduce MDA formation, and increase SOD activity. Moreover, lentinan showed not only a protective effect against oxidative damage but also reparative effects to a certain extent, in HaCaT cells. CONCLUSIONS: Our findings demonstrated the ability of lentinan to enhance cellular tolerance to oxidative damage, stress resistance, and to have protective and reparative effects on damaged cells. Therefore, with L. edodes as a source for antiaging substances, cosmetics with homology to foods have great potential clinical applications.

Washing away your sins in the brain: physical cleaning and priming of cleaning recruit different brain networks after moral threat.

The association between moral purity and physical cleanliness has been widely discussed recently. Studies found that moral threat initiates the need of physical cleanliness, but actual physical cleaning and priming of cleaning have inconsistent effects on subsequent attitudes and behaviors. Here, we used resting-state functional magnetic resonance imaging to explore the underlying neural mechanism of actual physical cleaning and priming of cleaning. After recalling moral transgression with strong feelings of guilt and shame, participants either actually cleaned their faces with a wipe or were primed with cleanliness through viewing its pictures. Results showed that actual physical cleaning reduced the spontaneous brain activities in the right insula and MPFC, regions that involved in embodied moral emotion processing, while priming of cleaning decreased activities in the right superior frontal gyrus and middle frontal gyrus, regions that participated in executive control processing. Additionally, actual physical cleaning also changed functional connectivity between insula/MPFC and emotion related regions, whereas priming of cleaning modified connectivity within both moral and sensorimotor areas. These findings revealed that actual physical cleaning and priming of cleaning led to changes in different brain regions and networks, providing neural evidence for the inconsistent effects of cleanliness on subsequent attitudes and behaviors.